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mikejuk writes "A Spanish research team have patented a video camera and algorithm that can tell the difference between males and females based on just a 25x25 pixel image. This means that there is enough information in such low resolution images to do the job! They also demonstrate that an old AI method, linear discriminant analysis, is as good and sometimes better than more trendy methods such as Support Vector Machines..."

I thought this story might be believable until I looked at the page. I'm not 100% sure what gender the 2nd row, 4th from the left person is and by the way, I'm a human. So I think the rest of the title to this story is "an arbitrarily acceptable percentage of the time so oh just publish it, it sounds neat"

And I'm laughing my ass off. I knew/. "readers" rarely RTFA, but I always assumed they were just too lazy to follow the links. I never realized they actually click the links, but only to look at the pretty pictures. xD

The article has a histogram that shows how sure the algorithm was of its predictions for both sexes. Males on the left of 0 were misclassified, and vice versa for females.

Now, the only confusing this is if that plot is for the test set of the train set. If it is for the test set then it answers your question. If it is for the train set it tells us a lot less. Pretty sloppy of them to title a graph with both:(

That's exactly the kind of sloppy thinking that had us "remediating" software for three years prior to Y2K. Where, in your grand scheme of things, are the values for (as examples): Michael Jackson, Lady Gaga and Richard Simmons? Please, won't somebody think of the mutants?

It's not like using linear discriminant analysis is some crazy or countercultural thing. It's a common simple technique. On some data it works well, and on such data, it's not uncommon to use it. It's particularly common in image-identification type tasks, and is one of the classic approaches to face recognition.

that an application of a standard machine learning method can be patented. They have a publication in a good journal (PAMI), but there is nothing earth-shattering in the research. As far as the comparison with SVM is concerned, non-linear SVM does beat the linear methods when there is enough data (as they acknowledge in the paper).

Quadratic programming with an RBF or Gaussian kernel should give you the best possible separation between any two classes by design, with sufficient amount of cross validation. Sadly, this doesn't always work in practice. I spent many months working on getting SVM to classify speech datasets, but the simpler methods always reigned. Not to mention, they take a fraction of the time to train a model.

I am guessing that the parameter tweaking required for SVM in some datasets is much more sensitive than others

I can't remember where I saw it, so can't give you a link, but there's a video of two people in a completely dark room with small light sources at joints and extremities. The instant they start moving, you can tell which one's the man and which one's the woman.

The algorithm is also interesting in that it proves that an older and fundamental pattern recognition technique - linear discriminant analysis is just as good as the more trendy Support Vector Machines if used correctly and much more efficient.

A bit of clarity might be useful here. Support vector machines use linear discriminants as the central part of the algorithm. These linear discriminates -- simply hyperplanes separating two regions, are defined by a subset of the data points (called the support vectors). The other key part of an SVM is that it projects the data into a high-dimensional space in which hyperplanes can appear as curves or other shapes in the original space. This higher dimensional space is determined from the data using distances between the points in the data set (it's a kernel space).

The net result of all this is that SVMs are pretty much guaranteed to always perform better in terms of misclassification error than a simple linear discriminant, as every possible linear discriminant is considered in building the SVM. But it can be slower, and it can overfit.

So what's going on here? Linear discriminant analysis is an old statistical technique (1930s) that fixes a hyperplane based on distributional assumptions about the two classes. This allows the two classes to be plotted in a simple histogram by projecting them to the normal of this hyperplane, as shown in the picture in the article. It's used all over in statistics, and it works very well when dealing with two symmetric Gaussian distributions (that's what the theory assumes).

Thus the reason it works well here is that they've managed to transform their data in such a way that the two classes look like this sort of distribution. That's the insight here, not the choice of classifier. When the simplest model works, more complex techniques will overfit, meaning that you train on noise instead of the underlying structure of the data.

There are quite a few people who don't know anything about it. They don't know what an SVM is, nor what differentiates it from linear separation (aka Perceptrons). So any explanation is more than welcome, and the GP got rightly modded up. Perhaps an even more *obvious* explanation is needed. Why don't you write one?

A few times a year I see a person who I can't readily determine the gender of. I'd like to see if this algorithm can teach me a thing or two (I won't be so crass as to photograph the person and run PatApp on the image).

These things can never become truly 100% perfect as there's lots of people that will show up as statistical anomalies. There are for example people who suffer from hormonal imbalancies resulting in overly feminine looks in a male, or overly masculine looks in a female. Just as well transsexual people will be hard for these things: hormonal medication does not change skeletal features, but they change distribution of fat in the body, including face, and thus for a machine they'll like fall in the grey area between either gender. And how about intersexual people who are physically neither gender? I had a friend before who was IS and it just was really hard to tell from the looks what gender one should assume. Mentally she identified as female, but that can't obviously be told from a picture.

This also makes me wonder about the future.. I hope these "gender guessing machinery" do not become the norm in our society and public areas because they will lead to lots of issues with the aforementioned groups of people.

Just as well transsexual people will be hard for these things: hormonal medication does not change skeletal features,

Even so quite a few transsexuals undergo various forms of plastic surgery that certainly can change a lot of skeletal features. This can range from rhinoplasty, forehead contouring, chin reductions etc...

Just as well transsexual people will be hard for these things: hormonal medication does not change skeletal features,

Even so quite a few transsexuals undergo various forms of plastic surgery that certainly can change a lot of skeletal features. This can range from rhinoplasty, forehead contouring, chin reductions etc...

Only rich ones do, those cost helluva lot of money. I have one FtM and one MtF friend, neither of whom can afford such and are only on hormones, and belonging to a sexual minority group myself I hang out a lot in one of the local forums for HLGBTI people and so far I have not met anyone else either who would have had anything else done than hormone therapy.

And how about intersexual people who are physically neither gender? I had a friend before who was IS and it just was really hard to tell from the looks what gender one should assume. Mentally she identified as female, but that can't obviously be told from a picture.

It really differs among IS people. I am a hermaphrodite yet there is no way to tell this while I'm still wearing clothes. Everyone identifies me as being a regular female, even at the swimming pool. There are heaps of 'regular' women who would get IDed by this system as being men, making it inaccurate for regular men and women, and a huge mess for IS people. As for TS people, most MtF TSs I have seen would be identified as being male, and most FtM TSs as being female. As said, unless you are going to modify the skeletal structure of the face etc. taking hormones doesn't magically transform you into the other gender/sex.

TFA alludes to this issue with the "gallery of misidentifications", but doesn't get as far as asking surely the most important question: what exactly does this software claim to determine? It's clearly not "biological sex", because you can't determine that from a photo (even a full-body naked photo) -- what about the 1% of people who are born intersex [isna.org]? And it's definitely, definitely not gender, which you could only ascertain by asking that indivi